Towards a deep learning-based outlier detection approach in the context of streaming data

Abstract Uncommon observations that significantly vary from the norm are referred to as outliers. Outlier detection, which aims to detect unexpected behavior, is a critical topic that has attracted significant attention in a wide range of research areas and application domains, including video surveillance, network intrusion detection, disease outbreak detection, and others. Deep learning-based techniques for outlier detection have currently outperformed machine learning and shallow approaches on streaming data, which are big and complicated datasets. Despite the fact that deep learning has been successfully applied in a variety of application domains, developing an effective and appropriate model is a difficult task due to the dynamic nature and variations of real-world applications and data. Hence, this research proposes a novel deep learning model based on a deep neural network (DNN) to handle the outlier detection problem in the context of streaming data. The proposed DNN model is developed with multiple hidden layers to improve feature abstraction and capabilities. Extensive experiments performed on four real-world outlier benchmark datasets, available at the UCI repository, and comparisons to state-of-the-art approaches are used to evaluate the proposed model's performance. Experiment results demonstrate that it outperforms both machine learning algorithms and deep learning competitors, resulting in significant performance gains. Particularly, when compared to other algorithms, the evaluation results clearly demonstrated the efficacy of the proposed approach, with much higher accuracy, recall and f1-score rates of 99.63%, 99.014% and 99.437%, respectively.